Mammography has been used in screening for breast cancer. The goal of screening mammography is the early detection of breast cancer through detection of masses and/or microcalcifications. Mammograms use doses of ionizing radiation usually at a lower-energy X-ray level (usually around 30 kVp).
The U.S. Preventive Services Task Force (USPSTF) recommended in 2009 screening of women aged between 50 and 74 with mammography every two years. The Canadian Task Force on Preventive Health Care and the European Cancer Observatory recommends mammography every 2-3 years between 50 and 69.
Three known challenges associated with mammography screening are: (1) false positive results (high recall rates), (2) false negative results, and (3) radiation exposure. False positives can lead to “overdiagnosis/overtreatment”. A ten-year cumulative risk of a false-positive biopsy result in annual mammography screening is 7% [1]. A significant, if not the main, cause of false-negative results is high breast density that obscures breast tumors in 2D projection mammograms [2]. To improve breast imaging, including regarding false positives and negatives, digital breast tomosynthesis [3, 4] was developed. Breast tomosynthesis is a 3D imaging technology that takes multiple projections over a limited anguler range and reconstructs a 3D volume of slices (images of breast slices). An advantage of breast tomosynthesis over conventional 2D mammography is that breast tomosynthesis reduces the problem of tissue overlap present in a 2D projection mammogram and improves lesion conspicuity especially for dense breasts. Breast tomosynthesis has shown to be a promising modality for early detection of breast cancer, with higher cancer detection rates and fewer patient recalls (false positives) [3, 5-7].
With breast tomosynthesis, a remaining major challenge in breast cancer screening is radiation exposure. The FDA approved two scenarios with breast tomosynthesis: 1) breast tomosynthesis combined with conventional 2D mammography and 2) breast tomosynthesis with synthesized 2D “mammograms” obtained from a 3D breast tomosynthesis volume. In the two scenarios, the radiation dose (5-8 mGy) [8] to the radiosensitive breasts in breast tomosynthesis can be 1.4-2.3 times higher than the conventional 2D digital mammography radiation dose (about 3.5 mGy). Repeated breast tomosynthesis for annual screening could increase cumulative radiation exposure and lifetime attributable risks for radiation-induced breast cancer. A recent study conducted by Drs. Martin Yaffe and James Mainprize estimated that 11 deaths from 86 radiation-induced breast cancers would occur within a cohort of 100,000 women each receiving a dose of 3.7 mSv during annual mammographic screening. Based on the study results [9, 10] for mammography screening, annual breast tomosynthesis screening of women starting at age 40 years could cause one life lost due to radiation-induced cancer per 18.9-47.4 lives saved. Therefore, radiation dose reduction is important for breast cancer screening with breast tomosynthesis. This patent specification discloses an innovative radiation dose reduction technology designed to help with the challenge of radiation dose in breast tomosynthesis.
When a radiologist or a computer detects, interprets, analyzes, and diagnoses breast images, there is a tradeoff between radiation dose levels and image quality. Higher radiation doses generally result in higher signal-to-noise ratio, while lower doses generally lead to increased image noise including quantum noise and electronic noise. Higher radiation exposure and dose would increase the risk of radiation-induced cancer. Therefore, it is important to reduce radiation exposures and dose as much as practicable and reasonably achievable.
Researchers have studied radiation dose reduction in mammography. S. Obenauer et al. [11] compared full-field digital mammography with screen-film mammography, and investigated a potential of dose reduction with an anthropomorphic breast phantom by changing anode-filter combinations in full-field digital mammography. R. L. Smathers et al. [12] evaluated the effects of anode-filter combinations on radiation dose reduction in mammography with 206 clinical cases. They showed that changing anode-filter combinations could reduce radiation dose by 35%. X. Liu et al. [13] investigated the effects of exposure equalization on the image quality and radiation dose reduction with an anthropomorphic breast phantom. They showed that the exposure equalization technique could reduce radiation dose by 34%. M. Yakabe et al. [14] investigated the relationship between radiation dose and the detectability of simulated microcalcifications with an anthropomorphic breast phantom. The radiation dose was changed by changing tube-current-time-product, mAs. W. Huda et al. [15] investigated the effects of random noise and lesion size on the detection performance by radiologists with an anthropomorphic breast phantom. A. S. Chawla et al. [16] investigated the effect of radiation dose reduction on the detection of breast lesions with simulated noise by using mathematical observer model analysis. G. Gennaro et al. [17] evaluated the phantom use in radiation dose reduction in mammography. N. T. Ranger et al. [18] investigated optimization of tube voltage, kVp and anode-filter combinations in mammography. E. Samei et al. [19] assessed the relationship between radiation dose and observer accuracy in the detection of simulated lesions in mammography. K. C. Young et al. [20] assessed the automatic beam quality selection function of a vendor's mammography system.
To help with the radiation dose issue in breast tomosynthesis, researchers have developed a technique to create a 2D image synthesized from 3D images, which can eliminate a necessity of separate acquisition of a 2D mammogram [21, 22]. Yaffe [23] pointed out, however, that even with use of the 2D synthetic image, radiation dose from 3D breast tomosynthesis can still be an issue, because radiation dose of 3D breast tomosynthesis can be higher than that of 2D mammography. In fact, the radiation dose by breast tomosynthesis with a 2D synthetic “mammogram” (5 mGy) [8] can still be 1.4 times higher than a typical mammography radiation dose. In addition, because the synthetically created 2D images may appear different from real 2D mammograms that radiologists have been accustomed to, acceptance of the synthetic images by radiologists may still be an issue. Therefore, it is important to reduce radiation dose of breast tomosynthesis, at least to the level of the mammography radiation dose. If radiation dose is simply reduced, more noise and artifact appear in breast tomosynthesis slices, which may obscure subtle lesions and patterns such as microcalcifications. A noise reduction filter may be used, but it tends to smooth out subtle patterns and tiny microcalcifications together with noise. In addition, it may not reduce artifact. Thus, it is a challenge to reduce noise and artifact in breast tomosynthesis slices while maintaining diagnostic information and depiction of subtle lesions. (The terms slice and slice image are used interchangeably in this patent specification, as is often done in breast tomosynthesis technology, although strictly speaking a breast slice is a physical object and a slice image is an image of that physical object.)
Computer-aided diagnostic (CAD) systems are being tested to decrease the number of cases of cancer that are missed in mammograms. In one test, a computer identified 71% of the cases of cancer that had been missed by physicians. However, the computer also flagged twice as many non-cancerous masses than the physicians did. In a second study of a larger set of mammograms, a computer recommended six biopsies that physicians did not. All six turned out to be cancers that would have been missed [24].
In the field of CAD, K. Suzuki et al. developed a pixel-based machine-learning technique based on an artificial neural network (ANN), called massive-training ANNs (MTANN), for distinguishing a specific opacity (pattern) from other opacities (patterns) in 2D CT images [25]. An MTANN was developed by extension of neural filters [26] and a neural edge enhancer [27] to accommodate various pattern-recognition and classification tasks [25]. The 2D MTANN was applied to reduction of false positives (FPs) in computerized detection of lung nodules on 2D CT slices in a slice-by-slice way [25, 28, 29] and in chest radiographs [30], the separation of ribs from soft tissue in chest radiographs [31-33], and the distinction between benign and malignant lung nodules on 2D CT slices [34]. For processing of three-dimensional (3D) volume data, a 3D MTANN was developed by extending the structure of the 2D MTANN, and it was applied to 3D CT colonography data [35-39].